You'll build systems that reason, plan, and act inside real materials discovery workflows — turning predictive models into reliable, operational decision-making agents that work directly with physical experiments and laboratory automation.
You'll sit at the intersection of AI research, software engineering, and lab science, embedding autonomy, safety, and observability into end-to-end discovery pipelines.
The company is a seed-stage deeptech startup operating in the AI-driven materials acceleration and cleantech space, with a small but highly experienced team and institutional backing.
This is an on-site role based in Berlin, Germany.
Right to work in Germany without employer visa sponsorship is required.
What You'll DoDesign and implement agentic systems that plan, reason, and act across materials discovery workflows involving experiments, simulations, and scientific datasets.
Build decision-making systems that select next actions under uncertainty and encode when autonomy should act versus when humans should stay in the loop.
Implement planning, control logic, and uncertainty-aware decision-making tailored to physical systems and experimental constraints.
Encode operational, experimental, and safety constraints directly into agent behavior; define stopping criteria, fallback strategies, and recovery mechanisms to prevent brittle behavior.
Integrate agents with laboratory automation and software systems so decisions translate into physical outcomes.
Instrument agents with logging, monitoring, and diagnostics to ensure observability and support debugging.
Build evaluation frameworks that assess decision quality, learning efficiency, and overall system behavior — going beyond model accuracy alone.
Analyze failure cases and it.